SEACrowd/code_mixed_jv_id

该数据集用于爪哇语和印度尼西亚语的情感分析和机器翻译任务。数据集包含混合代码的文本，支持情感分析和机器翻译两种任务。可以通过`datasets`库或`seacrowd`库加载数据集，并且提供了详细的加载方法。数据集的许可证为cc_by_3.0，并提供了相关的引用信息。

原始信息汇总

数据集概述

语言

• Javanese (jav)
• Indonesian (ind)

支持的任务

• 情感分析 (Sentiment Analysis)
• 机器翻译 (Machine Translation)

数据集版本

• 源版本: 1.0.0
• SEACrowd版本: 2024.06.20

数据集许可证

• cc_by_3.0

引用

如果使用 Code Mixed Jv Id 数据集，请引用以下内容：

@article{Tho_2021, doi = {10.1088/1742-6596/1869/1/012084}, url = {https://doi.org/10.1088/1742-6596/1869/1/012084}, year = 2021, month = {apr}, publisher = {{IOP} Publishing}, volume = {1869}, number = {1}, pages = {012084}, author = {C Tho and Y Heryadi and L Lukas and A Wibowo}, title = {Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach}, journal = {Journal of Physics: Conference Series}, abstract = {Nowadays mixing one language with another language either in spoken or written communication has become a common practice for bilingual speakers in daily conversation as well as in social media. Lexicon based approach is one of the approaches in extracting the sentiment analysis. This study is aimed to compare two lexicon models which are SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language. 3,963 tweets were gathered from two accounts that provide code-mixed tweets. Pre-processing such as removing duplicates, translating to English, filter special characters, transform lower case and filter stop words were conducted on the tweets. Positive and negative word score from lexicon model was then calculated using simple mathematic formula in order to classify the polarity. By comparing with the manual labelling, the result showed that SentiNetWord perform better than VADER in negative sentiments. However, both of the lexicon model did not perform well in neutral and positive sentiments. On overall performance, VADER showed better performance than SentiNetWord. This study showed that the reason for the misclassified was that most of Indonesian language and Javanese language consist of words that were considered as positive in both Lexicon model.} }

@article{lovenia2024seacrowd, title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, year={2024}, eprint={2406.10118}, journal={arXiv preprint arXiv: 2406.10118} }